metadata
datasets:
- multimolecule/rnacentral
library_name: multimolecule
license: agpl-3.0
mask_token: <mask>
pipeline_tag: fill-mask
tags:
- Biology
- RNA
- ncRNA
- rna
widget:
- example_title: microRNA 21
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: AUU
score: 0.036454
- label: GAU
score: 0.034317
- label: CUU
score: 0.0274
- label: AAG
score: 0.024828
- label: UUG
score: 0.02376
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: UAGCUUAUCAGACUG<mask>UUG
- example_title: microRNA 146a
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: AAU
score: 0.059322
- label: AAC
score: 0.039853
- label: GAU
score: 0.038141
- label: AAA
score: 0.036878
- label: GGA
score: 0.025595
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: UGAGAACUGAAUUCC<mask>GGU
- example_title: microRNA 155
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: UGU
score: 0.030609
- label: AUU
score: 0.02823
- label: UUU
score: 0.027554
- label: AAU
score: 0.027152
- label: GAA
score: 0.02533
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: UUAAUGCUAAUCGUG<mask>GGGGUU
- example_title: RNA component of mitochondrial RNA processing endoribonuclease
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: UUC
score: 0.030126
- label: CUC
score: 0.027686
- label: UUU
score: 0.027425
- label: UCU
score: 0.024937
- label: CUG
score: 0.024189
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGUUCGUGCUGA<mask>CCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAA
- example_title: 7SK small nuclear RNA
mask_index: 24
mask_index_1based: 25
masked_char: A
output:
- label: GGC
score: 0.052339
- label: GCC
score: 0.049248
- label: CCC
score: 0.040614
- label: UCC
score: 0.037157
- label: GAG
score: 0.031449
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGAUGUGAGGGCGAUCUGGCUGCG<mask>UCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUU
- example_title: telomerase RNA component
mask_index: 36
mask_index_1based: 37
masked_char: A
output:
- label: UGG
score: 0.082349
- label: GCC
score: 0.065632
- label: UGC
score: 0.034984
- label: GUG
score: 0.030916
- label: GCG
score: 0.027791
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCC<mask>UUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUG
- example_title: vault RNA 2-1
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: UUC
score: 0.036486
- label: GGC
score: 0.036387
- label: UCC
score: 0.033614
- label: GGA
score: 0.032683
- label: UGG
score: 0.02959
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
CGGGUCGGAGUU<mask>UCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
mask_index: 18
mask_index_1based: 19
masked_char: A
output:
- label: GGG
score: 0.058228
- label: CGG
score: 0.029714
- label: GCC
score: 0.025698
- label: GCU
score: 0.023683
- label: UGG
score: 0.023109
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: >-
GGCCGGGCGCGGUGGCUC<mask>CCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCU
- example_title: HIV-1 TAR-WT
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: UGG
score: 0.055933
- label: UCC
score: 0.039563
- label: GAG
score: 0.034423
- label: GGA
score: 0.027744
- label: AUG
score: 0.024329
pipeline_tag: fill-mask
sequence_type: ncRNA
task: fill-mask
text: GGUCUCUCUGGUUAG<mask>AGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
- example_title: prion protein (Kanno blood group)
mask_index: 21
mask_index_1based: 22
masked_char: A
output:
- label: CUG
score: 0.083667
- label: UUC
score: 0.058037
- label: GUG
score: 0.042056
- label: UUU
score: 0.039099
- label: UAU
score: 0.032602
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: AUGGCGAACCUUGGCUGCUGG<mask>CUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC
- example_title: interleukin 10
mask_index: 39
mask_index_1based: 40
masked_char: A
output:
- label: CUG
score: 0.178429
- label: AUG
score: 0.133451
- label: CUC
score: 0.077188
- label: AGC
score: 0.061544
- label: GCC
score: 0.056949
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUG<mask>GGGGUGAGGGCC
- example_title: Zaire ebolavirus
mask_index: 45
mask_index_1based: 46
masked_char: A
output:
- label: GAA
score: 0.044874
- label: GAU
score: 0.039572
- label: UUU
score: 0.035055
- label: GUU
score: 0.033566
- label: AUU
score: 0.028965
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCU<mask>GCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU
- example_title: SARS coronavirus
mask_index: 24
mask_index_1based: 25
masked_char: A
output:
- label: UUU
score: 0.153463
- label: UUG
score: 0.086199
- label: CUU
score: 0.064142
- label: UUA
score: 0.058406
- label: UUC
score: 0.049124
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGUUUAUUUUCUUAUUAUUUCUU<mask>CUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU
- example_title: insulin
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: AUG
score: 0.999765
- label: AUC
score: 0.000225
- label: CCU
score: 0.000003
- label: ACA
score: 0.000002
- label: GUG
score: 0.000001
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGGCCCUGUGG<mask>CGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG
- example_title: cyclin dependent kinase inhibitor 2A
mask_index: 18
mask_index_1based: 19
masked_char: A
output:
- label: GGC
score: 0.313019
- label: GCG
score: 0.094953
- label: GAC
score: 0.050381
- label: CUG
score: 0.04516
- label: GGA
score: 0.044861
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGGAGCCGGCGGCGGGG<mask>AGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA
- example_title: human papillomavirus type 16 E6
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: AAA
score: 0.042913
- label: CCA
score: 0.039063
- label: AAG
score: 0.034515
- label: GAA
score: 0.030832
- label: AGA
score: 0.024598
pipeline_tag: fill-mask
sequence_type: mRNA
task: fill-mask
text: >-
AUGCACCAAAAG<mask>ACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA
- example_title: NRAS proto-oncogene
mask_index: 36
mask_index_1based: 37
masked_char: A
output:
- label: CGG
score: 0.038451
- label: CCG
score: 0.037105
- label: UCG
score: 0.036003
- label: AUG
score: 0.034392
- label: ACU
score: 0.032722
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUC<mask>GCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUG
- example_title: amyloid beta precursor protein
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: GGC
score: 0.163655
- label: GGA
score: 0.075258
- label: GGU
score: 0.061764
- label: CGC
score: 0.044964
- label: GAC
score: 0.033283
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
GUCAGUUUCCUCGGC<mask>GGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG
- example_title: RUNX family transcription factor 1
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: AGA
score: 0.044242
- label: AAA
score: 0.042336
- label: AUG
score: 0.039409
- label: AAG
score: 0.036489
- label: UGG
score: 0.032637
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
ACUUCUUUGGGCCUC<mask>AACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAG
- example_title: fragile X messenger ribonucleoprotein 1
mask_index: 15
mask_index_1based: 16
masked_char: A
output:
- label: GCG
score: 0.104931
- label: GGC
score: 0.090209
- label: GGG
score: 0.040038
- label: AGC
score: 0.037217
- label: GUC
score: 0.031719
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
CUCAGUCAGGCGCUC<mask>UCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG
- example_title: MYC proto-oncogene
mask_index: 39
mask_index_1based: 40
masked_char: A
output:
- label: GCC
score: 0.080427
- label: GCG
score: 0.072732
- label: GGC
score: 0.069955
- label: GAG
score: 0.051612
- label: UGG
score: 0.041913
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCG<mask>GGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG
- example_title: activating transcription factor 4
mask_index: 24
mask_index_1based: 25
masked_char: A
output:
- label: CCC
score: 0.054324
- label: CCG
score: 0.039298
- label: CUC
score: 0.034159
- label: CCU
score: 0.030659
- label: CCA
score: 0.0273
pipeline_tag: fill-mask
sequence_type: 5' UTR
task: fill-mask
text: >-
CAUUUCUACUUUGCCCGCCCACAG<mask>UAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC
- example_title: Human GPI protein p137
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: AAA
score: 0.055483
- label: AUU
score: 0.045112
- label: AAU
score: 0.034645
- label: UUU
score: 0.032741
- label: AAG
score: 0.029208
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
UUUUUAAAAGGA<mask>GAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC
- example_title: nucleophosmin 1
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: AAA
score: 0.047198
- label: UCA
score: 0.041406
- label: AAU
score: 0.032262
- label: UUU
score: 0.031668
- label: AUU
score: 0.031399
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
GAAAAUAGUUUA<mask>AAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUU
- example_title: superoxide dismutase 1
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: AAG
score: 0.044194
- label: AAA
score: 0.032457
- label: AUU
score: 0.027957
- label: AAU
score: 0.023298
- label: CUC
score: 0.023021
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
ACAUUCCCUUGG<mask>UAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUA
- example_title: hemoglobin subunit alpha 2
mask_index: 42
mask_index_1based: 43
masked_char: A
output:
- label: UCG
score: 0.108342
- label: CCA
score: 0.074268
- label: CCG
score: 0.053831
- label: GCU
score: 0.032195
- label: CCU
score: 0.030543
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCC<mask>GGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGC
- example_title: BRAF proto-oncogene
mask_index: 12
mask_index_1based: 13
masked_char: A
output:
- label: AAA
score: 0.05836
- label: GAA
score: 0.055404
- label: AUG
score: 0.05109
- label: GAG
score: 0.048152
- label: UUG
score: 0.041588
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: >-
AACAAAUGAGUG<mask>GAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUG
- example_title: H3 clustered histone 1
mask_index: 24
mask_index_1based: 25
masked_char: A
output:
- label: AAA
score: 0.047706
- label: UUC
score: 0.040209
- label: CAA
score: 0.032907
- label: AUC
score: 0.027633
- label: AGA
score: 0.027074
pipeline_tag: fill-mask
sequence_type: 3' UTR
task: fill-mask
text: UUACUGUGGUCUCUCUGACGGUCC<mask>CAAAGGCUCUUUUCAGAGCCACCACCUUUU
RNA-FM
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective.
Disclaimer
This is an UNOFFICIAL implementation of the Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions by Jiayang Chen, Zhihang Hu, Siqi Sun, et al.
The OFFICIAL repository of RNA-FM is at ml4bio/RNA-FM.
The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
The team releasing RNA-FM did not write this model card for this model so this model card has been written by the MultiMolecule team.
Model Details
RNA-FM is a bert-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the Training Details section for more information on the training process.
Variants
- multimolecule/rnafm: The RNA-FM model pre-trained on non-coding RNA sequences.
- multimolecule/mrnafm: The RNA-FM model pre-trained on messenger RNA sequences.
Model Specification
| Variants | Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens |
|---|---|---|---|---|---|---|---|---|
| mRNA-FM | 12 | 1280 | 20 | 5120 | 239.26 | 258.08 | 128.85 | 1024 |
| RNA-FM | 640 | 99.52 | 109.02 | 54.36 |
Links
- Code: multimolecule.rnafm
- Data: multimolecule/rnacentral
- Paper: Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
- Developed by: Jiayang Chen, Zhihang Hu, Siqi Sun, Qingxiong Tan, Yixuan Wang, Qinze Yu, Licheng Zong, Liang Hong, Jin Xiao, Tao Shen, Irwin King, Yu Li
- Model type: BERT - ESM
- Original Repository: ml4bio/RNA-FM
Usage
The model file depends on the multimolecule library. You can install it using pip:
pip install multimolecule
Direct Use
Masked Language Modeling
You can use this model directly with a pipeline for masked language modeling:
import multimolecule # you must import multimolecule to register models
from transformers import pipeline
predictor = pipeline("fill-mask", model="multimolecule/rnafm")
output = predictor("gguc<mask>cucugguuagaccagaucugagccu")
Downstream Use
Extract Features
Here is how to use this model to get the features of a given sequence in PyTorch:
from multimolecule import RnaTokenizer, RnaFmModel
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmModel.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
output = model(**input)
Sequence Classification / Regression
This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.
Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:
import torch
from multimolecule import RnaTokenizer, RnaFmForSequencePrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForSequencePrediction.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])
output = model(**input, labels=label)
Token Classification / Regression
This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.
Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:
import torch
from multimolecule import RnaTokenizer, RnaFmForTokenPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForTokenPrediction.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))
output = model(**input, labels=label)
Contact Classification / Regression
This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.
Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:
import torch
from multimolecule import RnaTokenizer, RnaFmForContactPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/rnafm")
model = RnaFmForContactPrediction.from_pretrained("multimolecule/rnafm")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))
output = model(**input, labels=label)
Training Details
RNA-FM used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.
Training Data
The RNA-FM model was pre-trained on RNAcentral. RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of Expert Databases representing a broad range of organisms and RNA types.
RNA-FM applied CD-HIT (CD-HIT-EST) with a cut-off at 100% sequence identity to remove redundancy from the RNAcentral. The final dataset contains 23.7 million non-redundant RNA sequences.
RNA-FM preprocessed all tokens by replacing "U"s with "T"s.
Note that during model conversions, "T" is replaced with "U". [RnaTokenizer][multimolecule.RnaTokenizer] will convert "T"s to "U"s for you, you may disable this behaviour by passing replace_T_with_U=False.
Training Procedure
Preprocessing
RNA-FM used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:
- Mask rate: 15%
- Replacement:
<mask>for 80% of masked tokens - Replacement: random token for 10% of masked tokens
- Replacement: unchanged token for 10% of masked tokens
Pre-training
The model was trained on 8 NVIDIA A100 GPUs with 80GiB memories.
- Learning rate: 1e-4
- Learning rate scheduler: Inverse square root
- Learning rate warm-up: 10,000 steps
- Weight decay: 0.01
Citation
@article{chen2022interpretable,
title={Interpretable rna foundation model from unannotated data for highly accurate rna structure and function predictions},
author={Chen, Jiayang and Hu, Zhihang and Sun, Siqi and Tan, Qingxiong and Wang, Yixuan and Yu, Qinze and Zong, Licheng and Hong, Liang and Xiao, Jin and King, Irwin and others},
journal={arXiv preprint arXiv:2204.00300},
year={2022}
}
The artifacts distributed in this repository are part of the MultiMolecule project. If MultiMolecule supports your research, please cite the MultiMolecule project as follows:
@software{chen_2024_12638419,
author = {Chen, Zhiyuan and Zhu, Sophia Y.},
title = {MultiMolecule},
doi = {10.5281/zenodo.12638419},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.12638419},
year = 2024,
month = may,
day = 4
}
Contact
Please use GitHub issues of MultiMolecule for any questions or comments on the model card.
Please contact the authors of the RNA-FM paper for questions or comments on the paper/model.
License
This model implementation is licensed under the GNU Affero General Public License.
For additional terms and clarifications, please refer to our License FAQ.
SPDX-License-Identifier: AGPL-3.0-or-later